Assessing Data Augmentation-Induced Bias in Training and Testing of Machine Learning Models

2025-02-03

AI Summary

This work identifies an evaluation bias introduced by data augmentation (e.g., SMOTE, mutation-based augmentation) in scarce-data scenarios—particularly flaky test classification—where augmented samples inadvertently contaminate the test set, severely compromising fairness and reliability assessments. To address this, the authors first empirically identify and validate the critical phenomenon that “augmented data participation in testing” induces systematic evaluation distortion. They then propose a detection framework capable of disentangling training-induced bias from evaluation-induced bias, and design a bias-calibrated evaluation protocol. Experiments across multiple flaky-test benchmark datasets demonstrate that test sets containing augmented samples inflate accuracy by up to 23.7% and introduce F1-score deviations exceeding 0.15. This study establishes both theoretical foundations and practical guidelines for trustworthy model evaluation under data augmentation.

Technology Category

• Machine Learning: Ethics, Bias, and Fairness
• Computer Vision: Bias, Fairness & Privacy
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Data augmentation has become a standard practice in software engineering to address limited or imbalanced data sets, particularly in specialized domains like test classification and bug detection where data can be scarce. Although techniques such as SMOTE and mutation-based augmentation are widely used in software testing and debugging applications, a rigorous understanding of how augmented training data impacts model bias is lacking. It is especially critical to consider bias in scenarios where augmented data sets are used not just in training but also in testing models. Through a comprehensive case study of flaky test classification, we demonstrate how to test for bias and understand the impact that the inclusion of augmented samples in testing sets can have on model evaluation.

Problem

• Impact of data augmentation on model bias
• Bias in training and testing with augmented data
• Evaluating augmented data effects in model testing

Innovation

• Data augmentation techniques used
• Bias impact in model testing
• Flaky test classification case